## How to construct a numpy array of numbers of powers of 2 more elegantly?

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I would like to figure out how i can create this array without putting every single value in per hand.

Is there a way how i can use the information that every value is the doubled value of its predecessor, except for the first?

My Code is as follows:

```import numpy as np

Matrix = np.array([1,2,4,8,16,32,64,128,256]).reshape (3,3)

print(Matrix)

```

You can use `np.arange`, and take advantage of the fact that they are powers of `2`:

```2**np.arange(9).reshape(-1, 3)

array([[  1,   2,   4],
[  8,  16,  32],
[ 64, 128, 256]], dtype=int32)
```

Generate a NumPy array with powers of 2, Usually when creating certain sequences of numbers, python and numpy offer some syntactic sugar to do so in a simple way, without generating� An array that has 1-D arrays as its elements is called a 2-D array. These are often used to represent matrix or 2nd order tensors. NumPy has a whole sub module dedicated towards matrix operations called numpy.mat

You could also do something like this:

```var myRandomArray = ;
var i = 1;
var num = 1;
while (i < 9) {
myRandomArray.push(num = num * 2);
i = i + 1;
}
```

This is written in JavaScript. For Python, just switch what you need around, the main idea is still there. I believe in Python, it is append instead of push.

NumPy arange(): How to Use np.arange() – Real Python, by Mirko Stojiljković 2 Comments data-science intermediate Creating NumPy arrays is important when you're working with other Python libraries that rely The arguments of NumPy arange() that define the values contained in the array However, the variant with the negative value of step is more elegant and concise. Let's talk about creating a two-dimensional array. If you only use the arange function, it will output a one-dimensional array. To make it a two-dimensional array, chain its output with the reshape function. 1 2 array = np.arange(20).reshape(4,5) array.

You could use `np.vander`:

```np.vander(, 9, True).reshape(3, 3)
# array([[  1,   2,   4],
#        [  8,  16,  32],
#        [ 64, 128, 256]])
```

numpy.power — NumPy v1.19 Manual, Raise each base in x1 to the positionally-corresponding power in x2. x1 and Note that an integer type raised to a negative integer power will raise a x1 = range(6) >>> x1 [0, 1, 2, 3, 4, 5] >>> np.power(x1, 3) array([ 0, 1, 8,� numpy.power¶ numpy.power (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True [, signature, extobj]) = <ufunc 'power'>¶ First array elements raised to powers from second array, element-wise. Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same

Here's a jury-rigged solution:

```In : total_num = 9

In : np.array([2**n for n in range(0, total_num)]).reshape(3, -1)
Out:
array([[  1,   2,   4],
[  8,  16,  32],
[ 64, 128, 256]])
```

1. Elegant NumPy: The Foundation of Scientific Python, We will be working toward this goal throughout this chapter and Chapter 2, learning RPKM = (10^9 * C) / (N * L) Where: C = Number of reads mapped to a gene N power to predict biological outcomes, rather than to make specific statements NumPy arrays can represent data that has even more dimensions, such as� Creating numpy array from python list or nested lists You can create numpy array casting python list. Simply pass the python list to np.array () method as an argument and you are done. This will return 1D numpy array or a vector.

[PDF] Guide to NumPy, 12 New Python Types and C-Structures. 207 14.4.2 Creating a brand-new ndarray . number of bits as the int array data type on your platform. and grew to admire it's simple but elegant structures that grew out of the Indexing is a powerful tool in Python and NumPy takes full advantage of this power. Don’t forget that you can also influence the memory used for your arrays by specifying NumPy dtypes with the parameter dtype. Conclusion. You now know how to use NumPy arange(). The function np.arange() is one of the fundamental NumPy routines often used to create instances of NumPy ndarray. It has four arguments: start: the first value of

Print the shape of a 2-D array: import numpy as np. arr = np.array ( [ [1, 2, 3, 4], [5, 6, 7, 8]]) print(arr.shape) Try it Yourself ». The example above returns (2, 4), which means that the array has 2 dimensions, and each dimension has 4 elements.

Record arrays are structured arrays wrapped using a subclass of ndarray, numpy.recarray, which allows field access by attribute on the array object, and record arrays also use a special datatype, numpy.record, which allows field access by attribute on the individual elements of the array. The simplest way to create a record array is with numpy

• Maybe not more elegant, but a bitshift works in this case: `np.left_shift(1,np.arange(9)).reshape(3,3)`